This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. The course aim to cover statistical ideas that apply to managers. We will consider two basic themes: first, is recognizing and describing variations present in everything around us, and then modeling and making decisions in the presence of these variations. The fundamental concepts studied in this course will reappear in many other classes and business settings. Our focus will be on interpreting the meaning of the results in a business and managerial setting.
While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies. This will be accomplished through use of Excel and using data sets from many different disciplines, allowing you to see the use of statistics in very diverse settings. The course will focus not only on explaining these concepts but also understanding the meaning of the results obtained.
Upon successful completion of this course, you will be able to:
• Test for beliefs about a population..
• Compare differences between populations.
• Use linear regression model for prediction.
• Learn how to use Excel for statistical analysis.
This course is part of the iMBA offered by the University of Illinois, a flexible, fully-accredited online MBA at an incredibly competitive price. For more information, please see the Resource page in this course and onlinemba.illinois.edu.

De la lección

Module 4: Multiple Linear Regression

You are trying to predict next month’s sales numbers. You know that dozens, maybe even hundreds, of things like the weather, competitor’s promotions, rumors, etc. can impact the number. You talk to five people and each one has an idea about what makes the biggest impact, and the only thing they offer is “trust me.” Do you wish there was a better way of doing this rather than relying on blind faith? Well, there is. We can use Multiple Regression to sort through this mess and bring the focus to factors that really do matter.